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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 23 Dec 2011 09:55:34 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/23/t1324652160sy5oncecajxxqpu.htm/, Retrieved Mon, 29 Apr 2024 19:13:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=160475, Retrieved Mon, 29 Apr 2024 19:13:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact82

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2011-12-23 14:55:34] [aedc5b8e4f26bdca34b1a0cf88d6dfa2] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
1,2613		134,13		7,4481		9,1368		0,69215		8,5925		1,5657		1,6346		1,6374		1,8751		10,4399
1,2646		134,78		7,4511		9,1763		0,6769		8,7752		1,5734		1,6817		1,626		1,8262		10,4675
1,2262		133,13		7,4493		9,2346		0,67124		8,5407		1,567		1,6314		1,637		1,8566		10,149
1,1985		129,08		7,4436		9,1653		0,66533		8,2976		1,5547		1,6068		1,6142		1,8727		9,9163
1,2007		134,48		7,4405		9,1277		0,67157		8,2074		1,54		1,6541		1,7033		1,9484		9,9268
1,2138		132,86		7,4342		9,143		0,66428		8,2856		1,5192		1,6492		1,7483		1,9301		10,0529
1,2266		134,08		7,4355		9,1962		0,66576		8,4751		1,527		1,622		1,7135		1,8961		10,1622
1,2176		134,54		7,4365		9,1861		0,66942		8,3315		1,5387		1,6007		1,7147		1,8604		10,083
1,2218		134,51		7,4381		9,092		0,6813		8,3604		1,5431		1,5767		1,7396		1,8538		10,1134
1,249		135,97		7,4379		9,062		0,69144		8,2349		1,5426		1,56		1,7049		1,828		10,3423
1,2991		136,09		7,4313		8,998		0,69862		8,1412		1,5216		1,554		1,6867		1,854		10,7536
1,3408		139,14		7,4338		8,9819		0,695		8,2207		1,5364		1,6333		1,7462		1,8737		11,0967
1,3119		135,63		7,4405		9,0476		0,69867		8,2125		1,5469		1,606		1,7147		1,862		10,8588
1,3014		136,55		7,4427		9,0852		0,68968		8,3199		1,5501		1,6128		1,667		1,8192		10,7719
1,3201		138,83		7,4466		9,0884		0,69233		8,188		1,5494		1,6064		1,6806		1,8081		10,9262
1,2938		138,84		7,4499		9,167		0,68293		8,1763		1,5475		1,5991		1,6738		1,7967		10,708
1,2694		135,37		7,4443		9,1931		0,68399		8,0814		1,5448		1,5942		1,6571		1,7665		10,5062
1,2165		132,22		7,4448		9,2628		0,66895		7,8932		1,5391		1,5111		1,5875		1,7175		10,0683
1,2037		134,75		7,4584		9,4276		0,68756		7,92		1,5578		1,473		1,6002		1,7732		9,8954
1,2292		135,98		7,4596		9,3398		0,68527		7,9165		1,5528		1,4819		1,6144		1,7675		9,9589
1,2256		136,06		7,4584		9,3342		0,6776		7,8087		1,5496		1,4452		1,6009		1,7515		9,9177
1,2015		138,05		7,462		9,4223		0,68137		7,8347		1,549		1,4149		1,5937		1,7212		9,7189
1,1786		139,59		7,4596		9,5614		0,67933		7,8295		1,5449		1,3944		1,603		1,7088		9,5273
1,1856		140,58		7,4541		9,4316		0,67922		7,9737		1,5479		1,3778		1,5979		1,7072		9,5746
1,2103		139,82		7,4613		9,3111		0,68598		8,0366		1,5494		1,4025		1,6152		1,7616		9,763
1,1938		140,77		7,4641		9,3414		0,68297		8,0593		1,558		1,3723		1,6102		1,7741		9,6117
1,202		140,96		7,4612		9,4017		0,68935		7,9775		1,5691		1,3919		1,654		1,8956		9,6581
1,2271		143,59		7,4618		9,3346		0,69463		7,8413		1,5748		1,4052		1,6662		1,9733		9,8361
1,277		142,7		7,4565		9,331		0,6833		7,7988		1,5564		1,4173		1,6715		2,024		10,2353
1,265		145,11		7,4566		9,2349		0,68666		7,8559		1,5601		1,4089		1,7104		2,0462		10,1285
1,2684		146,7		7,4602		9,217		0,68782		7,9386		1,5687		1,4303		1,6869		2,0551		10,1347
1,2811		148,53		7,4609		9,2098		0,67669		7,992		1,5775		1,4338		1,6788		2,022		10,2141
1,2727		148,99		7,4601		9,2665		0,67511		8,2572		1,5841		1,4203		1,6839		1,9453		10,0971
1,2611		149,65		7,4555		9,2533		0,67254		8,396		1,5898		1,4235		1,6733		1,9066		9,9651
1,2881		151,11		7,4564		9,1008		0,67397		8,2446		1,5922		1,4635		1,6684		1,9263		10,1286
1,3213		154,82		7,4549		9,0377		0,67286		8,1575		1,5969		1,5212		1,6814		1,9094		10,3356
1,2999		156,56		7,4539		9,0795		0,66341		8,278		1,6155		1,5285		1,6602		1,8699		10,1238
1,3074		157,6		7,4541		9,1896		0,668		8,0876		1,6212		1,5309		1,6708		1,8859		10,1326
1,3242		155,24		7,4494		9,2992		0,68021		8,134		1,6124		1,5472		1,6704		1,8952		10,2467
1,3516		160,68		7,453		9,2372		0,67934		8,1194		1,6375		1,5334		1,6336		1,8394		10,44
1,3511		163,22		7,4519		9,2061		0,68136		8,1394		1,6506		1,4796		1,6378		1,8441		10,3689
1,3419		164,55		7,4452		9,329		0,67562		8,059		1,6543		1,4293		1,593		1,7738		10,2415
1,3716		166,76		7,441		9,1842		0,6744		7,938		1,6567		1,4417		1,5809		1,7446		10,3899
1,3622		159,05		7,4429		9,3231		0,67766		7,9735		1,6383		1,442		1,6442		1,8786		10,3162
1,3896		159,82		7,4506		9,2835		0,68887		7,8306		1,6475		1,4273		1,6445		1,9358		10,4533
1,4227		164,95		7,4534		9,1735		0,69614		7,6963		1,6706		1,3891		1,5837		1,8739		10,6741
1,4684		162,89		7,4543		9,2889		0,70896		7,9519		1,6485		1,4163		1,6373		1,9231		10,8957
1,457		163,55		7,4599		9,4319		0,72064		8,0117		1,6592		1,462		1,6703		1,893		10,7404
1,4718		158,68		7,4505		9,4314		0,74725		7,9566		1,6203		1,4862		1,6694		1,9054		10,6568
1,4748		157,97		7,454		9,3642		0,75094		7,948		1,608		1,474		1,6156		1,8513		10,5682
1,5527		156,59		7,4561		9,402		0,77494		7,9717		1,572		1,5519		1,6763		1,9344		10,9833
1,575		161,56		7,4603		9,3699		0,79487		7,9629		1,5964		1,5965		1,6933		1,996		11,0237
1,5557		162,31		7,4609		9,3106		0,79209		7,8648		1,6247		1,553		1,6382		2,0011		10,8462
1,5553		166,26		7,4586		9,3739		0,79152		7,9915		1,6139		1,5803		1,6343		2,0424		10,7287
1,577		168,45		7,4599		9,4566		0,79308		8,0487		1,6193		1,5974		1,6386		2,09		10,7809
1,4975		163,63		7,4595		9,3984		0,79279		7,9723		1,6212		1,5765		1,6961		2,1097		10,2609
1,437		153,2		7,4583		9,5637		0,79924		8,1566		1,5942		1,5201		1,7543		2,1293		9,8252
1,3322		133,52		7,4545		9,8506		0,78668		8,5928		1,5194		1,5646		1,9345		2,1891		9,1071
1,2732		123,28		7,4485		10,1275		0,83063		8,8094		1,5162		1,5509		1,9381		2,2554		8,695
1,3449		122,51		7,4503		10,7538		0,90448		9,4228		1,5393		1,66		2,0105		2,4119		9,2205
1,3239		119,73		7,4519		10,7264		0,91819		9,2164		1,4935		1,6233		1,9633		2,4132		9,0496
1,2785		118,3		7,4514		10,9069		0,88691		8,7838		1,4904		1,594		1,9723		2,4851		8,7406
1,305		127,65		7,4509		11,1767		0,91966		8,8388		1,5083		1,647		1,9594		2,4527		8,921
1,319		130,25		7,4491		10,8796		0,89756		8,7867		1,5147		1,6188		1,8504		2,3123		9,011
1,365		131,85		7,4468		10,582		0,88444		8,7943		1,5118		1,5712		1,783		2,2663		9,3157
1,4016		135,39		7,4457		10,8713		0,8567		8,9388		1,5148		1,5761		1,7463		2,1967		9,5786
1,4088		133,09		7,4458		10,8262		0,86092		8,9494		1,5202		1,5824		1,7504		2,1873		9,6246
1,4268		135,31		7,444		10,221		0,86265		8,6602		1,5236		1,5522		1,7081		2,1097		9,7485
1,4562		133,14		7,4428		10,1976		0,89135		8,5964		1,5148		1,5752		1,6903		2,0691		9,9431
1,4816		133,91		7,4438		10,3102		0,91557		8,3596		1,5138		1,5619		1,6341		2,0065		10,1152
1,4914		132,97		7,4415		10,3331		0,89892		8,4143		1,5105		1,5805		1,6223		2,045		10,1827
1,4614		131,21		7,4419		10,4085		0,89972		8,4066		1,5021		1,5397		1,6185		2,0383		9,9777
1,4272		130,34		7,4424		10,1939		0,88305		8,1817		1,4765		1,4879		1,5624		1,9646		9,7436
1,3686		123,46		7,444		9,9505		0,87604		8,0971		1,4671		1,4454		1,5434		1,9615		9,3462
1,3569		123,03		7,4416		9,7277		0,9016		8,0369		1,4482		1,3889		1,4882		1,9301		9,2623
1,3406		125,33		7,4428		9,6617		0,87456		7,9323		1,4337		1,3467		1,4463		1,8814		9,1505
1,2565		115,83		7,4413		9,6641		0,85714		7,8907		1,4181		1,306		1,4436		1,801		8,5794
1,2208		110,99		7,4409		9,5722		0,82771		7,9062		1,3767		1,2674		1,4315		1,7667		8,3245
1,277		111,73		7,4522		9,4954		0,83566		8,0201		1,346		1,3322		1,4586		1,7925		8,6538
1,2894		110,04		7,4495		9,4216		0,82363		7,9325		1,3413		1,3411		1,4337		1,8059		8,752
1,3067		110,26		7,4476		9,2241		0,83987		7,9156		1,3089		1,3515		1,3943		1,7955		8,8104
1,3898		113,67		7,4567		9,2794		0,87638		8,111		1,3452		1,4152		1,4164		1,8498		9,2665
1,3661		112,69		7,4547		9,3166		0,8551		8,1463		1,3442		1,3831		1,3813		1,7703		9,0895
1,322		110,11		7,4528		9,0559		0,84813		7,902		1,2811		1,3327		1,3304		1,7587		8,7873
1,336		110,38		7,4518		8,9122		0,84712		7,8199		1,2779		1,3277		1,3417		1,7435		8,8154
1,3649		112,77		7,4555		8,7882		0,84635		7,8206		1,2974		1,3484		1,3543		1,7925		8,9842
1,3999		114,4		7,4574		8,8864		0,86653		7,8295		1,2867		1,3672		1,3854		1,8877		9,1902
1,4442		120,42		7,4574		8,9702		0,88291		7,8065		1,2977		1,3834		1,3662		1,8331		9,4274
1,4349		116,47		7,4566		8,9571		0,87788		7,8384		1,2537		1,3885		1,3437		1,8024		9,3198
1,4388		115,75		7,4579		9,1125		0,88745		7,8302		1,2092		1,4063		1,3567		1,7666		9,3161
1,4264		113,26		7,456		9,134		0,88476		7,7829		1,1766		1,3638		1,3249		1,6877		9,2121
1,4343		110,43		7,4498		9,1655		0,87668		7,7882		1,1203		1,4071		1,3651		1,7108		9,1857
1,377		105,75		7,4462		9,1343		0,87172		7,7243		1,2005		1,3794		1,3458		1,6932		8,7994
1,3706		105,06		7,4442		9,1138		0,87036		7,7474		1,2295		1,3981		1,3525		1,7361		8,7308
1,3556		105,02		7,4412		9,1387		0,8574		7,7868		1,2307		1,3897		1,3414		1,7584		8,6154

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160475&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160475&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160475&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Multiple Linear Regression - Estimated Regression Equation
EUR/USD[t] = + 0.309173908189271 + 0.00547113591987539`EUR/JPY`[t] -0.0732152190415344`EUR/DAK`[t] -0.0427294912371332`EUR/SWK`[t] + 1.30494431434585`EUR/GBP`[t] + 0.00882211905829047`EUR/NOK`[t] -0.331036242803307`EUR/CHF`[t] + 0.057282817623541`EUR/CAD`[t] -0.0549529463886572`EUR/AUD`[t] + 0.00606260847246574`EUR/NZD`[t] + 0.0659618164617436`EUR/CHY`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
EUR/USD[t] =  +  0.309173908189271 +  0.00547113591987539`EUR/JPY`[t] -0.0732152190415344`EUR/DAK`[t] -0.0427294912371332`EUR/SWK`[t] +  1.30494431434585`EUR/GBP`[t] +  0.00882211905829047`EUR/NOK`[t] -0.331036242803307`EUR/CHF`[t] +  0.057282817623541`EUR/CAD`[t] -0.0549529463886572`EUR/AUD`[t] +  0.00606260847246574`EUR/NZD`[t] +  0.0659618164617436`EUR/CHY`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160475&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]EUR/USD[t] =  +  0.309173908189271 +  0.00547113591987539`EUR/JPY`[t] -0.0732152190415344`EUR/DAK`[t] -0.0427294912371332`EUR/SWK`[t] +  1.30494431434585`EUR/GBP`[t] +  0.00882211905829047`EUR/NOK`[t] -0.331036242803307`EUR/CHF`[t] +  0.057282817623541`EUR/CAD`[t] -0.0549529463886572`EUR/AUD`[t] +  0.00606260847246574`EUR/NZD`[t] +  0.0659618164617436`EUR/CHY`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160475&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160475&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
EUR/USD[t] = + 0.309173908189271 + 0.00547113591987539`EUR/JPY`[t] -0.0732152190415344`EUR/DAK`[t] -0.0427294912371332`EUR/SWK`[t] + 1.30494431434585`EUR/GBP`[t] + 0.00882211905829047`EUR/NOK`[t] -0.331036242803307`EUR/CHF`[t] + 0.057282817623541`EUR/CAD`[t] -0.0549529463886572`EUR/AUD`[t] + 0.00606260847246574`EUR/NZD`[t] + 0.0659618164617436`EUR/CHY`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.3091739081892712.8226370.10950.913040.45652
`EUR/JPY`0.005471135919875390.00045811.943300
`EUR/DAK`-0.07321521904153440.375136-0.19520.8457310.422866
`EUR/SWK`-0.04272949123713320.012192-3.50470.0007360.000368
`EUR/GBP`1.304944314345850.08811614.809400
`EUR/NOK`0.008822119058290470.0156550.56350.5745740.287287
`EUR/CHF`-0.3310362428033070.076215-4.34343.9e-051.9e-05
`EUR/CAD`0.0572828176235410.0553411.03510.3035960.151798
`EUR/AUD`-0.05495294638865720.057814-0.95050.3445760.172288
`EUR/NZD`0.006062608472465740.0431060.14060.8884870.444243
`EUR/CHY`0.06596181646174360.0091927.17600

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 0.309173908189271 & 2.822637 & 0.1095 & 0.91304 & 0.45652 \tabularnewline
`EUR/JPY` & 0.00547113591987539 & 0.000458 & 11.9433 & 0 & 0 \tabularnewline
`EUR/DAK` & -0.0732152190415344 & 0.375136 & -0.1952 & 0.845731 & 0.422866 \tabularnewline
`EUR/SWK` & -0.0427294912371332 & 0.012192 & -3.5047 & 0.000736 & 0.000368 \tabularnewline
`EUR/GBP` & 1.30494431434585 & 0.088116 & 14.8094 & 0 & 0 \tabularnewline
`EUR/NOK` & 0.00882211905829047 & 0.015655 & 0.5635 & 0.574574 & 0.287287 \tabularnewline
`EUR/CHF` & -0.331036242803307 & 0.076215 & -4.3434 & 3.9e-05 & 1.9e-05 \tabularnewline
`EUR/CAD` & 0.057282817623541 & 0.055341 & 1.0351 & 0.303596 & 0.151798 \tabularnewline
`EUR/AUD` & -0.0549529463886572 & 0.057814 & -0.9505 & 0.344576 & 0.172288 \tabularnewline
`EUR/NZD` & 0.00606260847246574 & 0.043106 & 0.1406 & 0.888487 & 0.444243 \tabularnewline
`EUR/CHY` & 0.0659618164617436 & 0.009192 & 7.176 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160475&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]0.309173908189271[/C][C]2.822637[/C][C]0.1095[/C][C]0.91304[/C][C]0.45652[/C][/ROW]
[ROW][C]`EUR/JPY`[/C][C]0.00547113591987539[/C][C]0.000458[/C][C]11.9433[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`EUR/DAK`[/C][C]-0.0732152190415344[/C][C]0.375136[/C][C]-0.1952[/C][C]0.845731[/C][C]0.422866[/C][/ROW]
[ROW][C]`EUR/SWK`[/C][C]-0.0427294912371332[/C][C]0.012192[/C][C]-3.5047[/C][C]0.000736[/C][C]0.000368[/C][/ROW]
[ROW][C]`EUR/GBP`[/C][C]1.30494431434585[/C][C]0.088116[/C][C]14.8094[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`EUR/NOK`[/C][C]0.00882211905829047[/C][C]0.015655[/C][C]0.5635[/C][C]0.574574[/C][C]0.287287[/C][/ROW]
[ROW][C]`EUR/CHF`[/C][C]-0.331036242803307[/C][C]0.076215[/C][C]-4.3434[/C][C]3.9e-05[/C][C]1.9e-05[/C][/ROW]
[ROW][C]`EUR/CAD`[/C][C]0.057282817623541[/C][C]0.055341[/C][C]1.0351[/C][C]0.303596[/C][C]0.151798[/C][/ROW]
[ROW][C]`EUR/AUD`[/C][C]-0.0549529463886572[/C][C]0.057814[/C][C]-0.9505[/C][C]0.344576[/C][C]0.172288[/C][/ROW]
[ROW][C]`EUR/NZD`[/C][C]0.00606260847246574[/C][C]0.043106[/C][C]0.1406[/C][C]0.888487[/C][C]0.444243[/C][/ROW]
[ROW][C]`EUR/CHY`[/C][C]0.0659618164617436[/C][C]0.009192[/C][C]7.176[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160475&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160475&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.3091739081892712.8226370.10950.913040.45652
`EUR/JPY`0.005471135919875390.00045811.943300
`EUR/DAK`-0.07321521904153440.375136-0.19520.8457310.422866
`EUR/SWK`-0.04272949123713320.012192-3.50470.0007360.000368
`EUR/GBP`1.304944314345850.08811614.809400
`EUR/NOK`0.008822119058290470.0156550.56350.5745740.287287
`EUR/CHF`-0.3310362428033070.076215-4.34343.9e-051.9e-05
`EUR/CAD`0.0572828176235410.0553411.03510.3035960.151798
`EUR/AUD`-0.05495294638865720.057814-0.95050.3445760.172288
`EUR/NZD`0.006062608472465740.0431060.14060.8884870.444243
`EUR/CHY`0.06596181646174360.0091927.17600






Multiple Linear Regression - Regression Statistics
Multiple R0.97667530750816
R-squared0.953894656296159
Adjusted R-squared0.948405924902845
F-TEST (value)173.791462533231
F-TEST (DF numerator)10
F-TEST (DF denominator)84
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0225522618014278
Sum Squared Residuals0.0427227790382519

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.97667530750816 \tabularnewline
R-squared & 0.953894656296159 \tabularnewline
Adjusted R-squared & 0.948405924902845 \tabularnewline
F-TEST (value) & 173.791462533231 \tabularnewline
F-TEST (DF numerator) & 10 \tabularnewline
F-TEST (DF denominator) & 84 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0225522618014278 \tabularnewline
Sum Squared Residuals & 0.0427227790382519 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160475&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.97667530750816[/C][/ROW]
[ROW][C]R-squared[/C][C]0.953894656296159[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.948405924902845[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]173.791462533231[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]10[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]84[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.0225522618014278[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.0427227790382519[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160475&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160475&T=3

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.97667530750816
R-squared0.953894656296159
Adjusted R-squared0.948405924902845
F-TEST (value)173.791462533231
F-TEST (DF numerator)10
F-TEST (DF denominator)84
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0225522618014278
Sum Squared Residuals0.0427227790382519






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.26131.2716674045655-0.0103674045654983
21.26461.257327172519960.00727282748004476
31.22621.214293973140640.0119060268593566
41.19851.174321284071930.024178715928074
51.20071.21687705252504-0.0161770525250446
61.21381.21133697743490.00246302256509904
71.22661.224022168737660.00257783126233848
81.21761.21980668121891-0.00220668121890916
91.22181.23706949615962-0.0152694961596155
101.2491.27453684858549-0.0255368485854857
111.29911.32185009115036-0.022750091150364
121.34081.35414384586336-0.0133438458633645
131.31191.31728716118542-0.00538716118542392
141.30141.30572885131265-0.00432885131265439
151.32011.32930360100527-0.0092036010052659
161.29381.29951097102213-0.00571097102213468
171.26941.268403557676140.00099644232385885
181.21651.198637623349620.0178623766503821
191.20371.20882563028746-0.00512563028746252
201.22921.22173840356930.00746159643070151
211.22561.20842756330380.0171724366961998
221.20151.20599780341479-0.00449780341478747
231.17861.19290586365931-0.014305863659309
241.18561.20684641585163-0.0212464158516276
251.21031.22941109890411-0.0191110989041095
261.19381.21517502327348-0.0213750232734798
271.2021.22029270739015-0.0182927073901468
281.22711.25361034490699-0.0265103449069861
291.2771.26725521135220.00974478864779688
301.2651.27867417592856-0.0136741759285636
311.26841.29025114512316-0.0218511451231586
321.28111.28923598215654-0.00813598215653775
331.27271.2782453767965-0.0055453767964963
341.26111.27056526597626-0.00946526597625876
351.28811.29820416521781-0.0101041652178125
361.32131.33367783630432-0.0123778363043212
371.29991.31143178402088-0.0115317840208796
381.30741.3150581049497-0.00765810494970298
391.32421.32560133543074-0.00140133543073699
401.35161.36182074543011-0.0102207454301063
411.35111.36762870084684-0.016528700846843
421.34191.35147071575872-0.00957071575871508
431.37161.37758960792583-0.00598960792582953
441.36221.332480952294460.0297190477055439
451.38961.356675872074280.0329241279257248
461.42271.405235267250530.0174647327494681
471.46841.428796104554550.0396038954454518
481.4571.428491979224410.0285080207755869
491.47181.445669384716010.0261306152839947
501.47481.449296580923730.0255034190762692
511.55271.512433779556870.040266220443133
521.5751.563200986684550.0117990133154547
531.55571.54479154923920.0109084507608035
541.55531.56209329968667-0.00679329968666991
551.5771.575673946764990.00132605323500831
561.49751.51160008502139-0.014100085021391
571.4371.4314918479640.00550815203600369
581.33221.269700566594490.06249943340551
591.27321.234842600715550.0383573992844526
601.34491.335753941397970.00914605860203141
611.32391.34205569496705-0.0181556949670507
621.27851.260827747726810.0176722522731932
631.3051.35323561906589-0.0482356190658892
641.3191.35832962694587-0.0393296269458735
651.3651.38467117177227-0.0196711717722702
661.40161.375057282987160.0265427170128407
671.40881.371319062370190.0374809376298102
681.42681.416334112392090.0104658876079136
691.45621.46023734019676-0.00403734019676425
701.48161.50271227210132-0.0211122721013212
711.49141.483006698883530.00839330111647364
721.46141.458192029897740.00320797010226181
731.42721.43152942923821-0.00432942923821397
741.36861.3697665239768-0.00116652397680068
751.35691.41026201393869-0.0533620139386886
761.34061.38638486575738-0.0457848657573813
771.25651.27614011702264-0.0196401170226437
781.22081.210485277033350.0103147229666521
791.2771.262630513074060.0143694869259367
801.28941.250256818296280.0391431817037234
811.30671.298357434098130.00834256590186939
821.38981.38418439108890.00561560891110238
831.36611.3381856643730.027914335627001
841.3221.32489256611531-0.00289256611531468
851.3361.332454235179780.003545764820222
861.36491.355028741892060.00987125810793545
871.39991.40309910426324-0.00319910426324288
881.44421.46728349425332-0.0230834942533212
891.43491.44881895639264-0.0139189563926349
901.43881.4651356300705-0.0263356300705027
911.42641.44957173678859-0.023171736788594
921.43431.44000641701975-0.00570641701975462
931.3771.356298978158920.0207010218410776
941.37061.338813404267040.0317865957329578
951.35561.31344051442730.0421594855726951

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.2613 & 1.2716674045655 & -0.0103674045654983 \tabularnewline
2 & 1.2646 & 1.25732717251996 & 0.00727282748004476 \tabularnewline
3 & 1.2262 & 1.21429397314064 & 0.0119060268593566 \tabularnewline
4 & 1.1985 & 1.17432128407193 & 0.024178715928074 \tabularnewline
5 & 1.2007 & 1.21687705252504 & -0.0161770525250446 \tabularnewline
6 & 1.2138 & 1.2113369774349 & 0.00246302256509904 \tabularnewline
7 & 1.2266 & 1.22402216873766 & 0.00257783126233848 \tabularnewline
8 & 1.2176 & 1.21980668121891 & -0.00220668121890916 \tabularnewline
9 & 1.2218 & 1.23706949615962 & -0.0152694961596155 \tabularnewline
10 & 1.249 & 1.27453684858549 & -0.0255368485854857 \tabularnewline
11 & 1.2991 & 1.32185009115036 & -0.022750091150364 \tabularnewline
12 & 1.3408 & 1.35414384586336 & -0.0133438458633645 \tabularnewline
13 & 1.3119 & 1.31728716118542 & -0.00538716118542392 \tabularnewline
14 & 1.3014 & 1.30572885131265 & -0.00432885131265439 \tabularnewline
15 & 1.3201 & 1.32930360100527 & -0.0092036010052659 \tabularnewline
16 & 1.2938 & 1.29951097102213 & -0.00571097102213468 \tabularnewline
17 & 1.2694 & 1.26840355767614 & 0.00099644232385885 \tabularnewline
18 & 1.2165 & 1.19863762334962 & 0.0178623766503821 \tabularnewline
19 & 1.2037 & 1.20882563028746 & -0.00512563028746252 \tabularnewline
20 & 1.2292 & 1.2217384035693 & 0.00746159643070151 \tabularnewline
21 & 1.2256 & 1.2084275633038 & 0.0171724366961998 \tabularnewline
22 & 1.2015 & 1.20599780341479 & -0.00449780341478747 \tabularnewline
23 & 1.1786 & 1.19290586365931 & -0.014305863659309 \tabularnewline
24 & 1.1856 & 1.20684641585163 & -0.0212464158516276 \tabularnewline
25 & 1.2103 & 1.22941109890411 & -0.0191110989041095 \tabularnewline
26 & 1.1938 & 1.21517502327348 & -0.0213750232734798 \tabularnewline
27 & 1.202 & 1.22029270739015 & -0.0182927073901468 \tabularnewline
28 & 1.2271 & 1.25361034490699 & -0.0265103449069861 \tabularnewline
29 & 1.277 & 1.2672552113522 & 0.00974478864779688 \tabularnewline
30 & 1.265 & 1.27867417592856 & -0.0136741759285636 \tabularnewline
31 & 1.2684 & 1.29025114512316 & -0.0218511451231586 \tabularnewline
32 & 1.2811 & 1.28923598215654 & -0.00813598215653775 \tabularnewline
33 & 1.2727 & 1.2782453767965 & -0.0055453767964963 \tabularnewline
34 & 1.2611 & 1.27056526597626 & -0.00946526597625876 \tabularnewline
35 & 1.2881 & 1.29820416521781 & -0.0101041652178125 \tabularnewline
36 & 1.3213 & 1.33367783630432 & -0.0123778363043212 \tabularnewline
37 & 1.2999 & 1.31143178402088 & -0.0115317840208796 \tabularnewline
38 & 1.3074 & 1.3150581049497 & -0.00765810494970298 \tabularnewline
39 & 1.3242 & 1.32560133543074 & -0.00140133543073699 \tabularnewline
40 & 1.3516 & 1.36182074543011 & -0.0102207454301063 \tabularnewline
41 & 1.3511 & 1.36762870084684 & -0.016528700846843 \tabularnewline
42 & 1.3419 & 1.35147071575872 & -0.00957071575871508 \tabularnewline
43 & 1.3716 & 1.37758960792583 & -0.00598960792582953 \tabularnewline
44 & 1.3622 & 1.33248095229446 & 0.0297190477055439 \tabularnewline
45 & 1.3896 & 1.35667587207428 & 0.0329241279257248 \tabularnewline
46 & 1.4227 & 1.40523526725053 & 0.0174647327494681 \tabularnewline
47 & 1.4684 & 1.42879610455455 & 0.0396038954454518 \tabularnewline
48 & 1.457 & 1.42849197922441 & 0.0285080207755869 \tabularnewline
49 & 1.4718 & 1.44566938471601 & 0.0261306152839947 \tabularnewline
50 & 1.4748 & 1.44929658092373 & 0.0255034190762692 \tabularnewline
51 & 1.5527 & 1.51243377955687 & 0.040266220443133 \tabularnewline
52 & 1.575 & 1.56320098668455 & 0.0117990133154547 \tabularnewline
53 & 1.5557 & 1.5447915492392 & 0.0109084507608035 \tabularnewline
54 & 1.5553 & 1.56209329968667 & -0.00679329968666991 \tabularnewline
55 & 1.577 & 1.57567394676499 & 0.00132605323500831 \tabularnewline
56 & 1.4975 & 1.51160008502139 & -0.014100085021391 \tabularnewline
57 & 1.437 & 1.431491847964 & 0.00550815203600369 \tabularnewline
58 & 1.3322 & 1.26970056659449 & 0.06249943340551 \tabularnewline
59 & 1.2732 & 1.23484260071555 & 0.0383573992844526 \tabularnewline
60 & 1.3449 & 1.33575394139797 & 0.00914605860203141 \tabularnewline
61 & 1.3239 & 1.34205569496705 & -0.0181556949670507 \tabularnewline
62 & 1.2785 & 1.26082774772681 & 0.0176722522731932 \tabularnewline
63 & 1.305 & 1.35323561906589 & -0.0482356190658892 \tabularnewline
64 & 1.319 & 1.35832962694587 & -0.0393296269458735 \tabularnewline
65 & 1.365 & 1.38467117177227 & -0.0196711717722702 \tabularnewline
66 & 1.4016 & 1.37505728298716 & 0.0265427170128407 \tabularnewline
67 & 1.4088 & 1.37131906237019 & 0.0374809376298102 \tabularnewline
68 & 1.4268 & 1.41633411239209 & 0.0104658876079136 \tabularnewline
69 & 1.4562 & 1.46023734019676 & -0.00403734019676425 \tabularnewline
70 & 1.4816 & 1.50271227210132 & -0.0211122721013212 \tabularnewline
71 & 1.4914 & 1.48300669888353 & 0.00839330111647364 \tabularnewline
72 & 1.4614 & 1.45819202989774 & 0.00320797010226181 \tabularnewline
73 & 1.4272 & 1.43152942923821 & -0.00432942923821397 \tabularnewline
74 & 1.3686 & 1.3697665239768 & -0.00116652397680068 \tabularnewline
75 & 1.3569 & 1.41026201393869 & -0.0533620139386886 \tabularnewline
76 & 1.3406 & 1.38638486575738 & -0.0457848657573813 \tabularnewline
77 & 1.2565 & 1.27614011702264 & -0.0196401170226437 \tabularnewline
78 & 1.2208 & 1.21048527703335 & 0.0103147229666521 \tabularnewline
79 & 1.277 & 1.26263051307406 & 0.0143694869259367 \tabularnewline
80 & 1.2894 & 1.25025681829628 & 0.0391431817037234 \tabularnewline
81 & 1.3067 & 1.29835743409813 & 0.00834256590186939 \tabularnewline
82 & 1.3898 & 1.3841843910889 & 0.00561560891110238 \tabularnewline
83 & 1.3661 & 1.338185664373 & 0.027914335627001 \tabularnewline
84 & 1.322 & 1.32489256611531 & -0.00289256611531468 \tabularnewline
85 & 1.336 & 1.33245423517978 & 0.003545764820222 \tabularnewline
86 & 1.3649 & 1.35502874189206 & 0.00987125810793545 \tabularnewline
87 & 1.3999 & 1.40309910426324 & -0.00319910426324288 \tabularnewline
88 & 1.4442 & 1.46728349425332 & -0.0230834942533212 \tabularnewline
89 & 1.4349 & 1.44881895639264 & -0.0139189563926349 \tabularnewline
90 & 1.4388 & 1.4651356300705 & -0.0263356300705027 \tabularnewline
91 & 1.4264 & 1.44957173678859 & -0.023171736788594 \tabularnewline
92 & 1.4343 & 1.44000641701975 & -0.00570641701975462 \tabularnewline
93 & 1.377 & 1.35629897815892 & 0.0207010218410776 \tabularnewline
94 & 1.3706 & 1.33881340426704 & 0.0317865957329578 \tabularnewline
95 & 1.3556 & 1.3134405144273 & 0.0421594855726951 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160475&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]1.2613[/C][C]1.2716674045655[/C][C]-0.0103674045654983[/C][/ROW]
[ROW][C]2[/C][C]1.2646[/C][C]1.25732717251996[/C][C]0.00727282748004476[/C][/ROW]
[ROW][C]3[/C][C]1.2262[/C][C]1.21429397314064[/C][C]0.0119060268593566[/C][/ROW]
[ROW][C]4[/C][C]1.1985[/C][C]1.17432128407193[/C][C]0.024178715928074[/C][/ROW]
[ROW][C]5[/C][C]1.2007[/C][C]1.21687705252504[/C][C]-0.0161770525250446[/C][/ROW]
[ROW][C]6[/C][C]1.2138[/C][C]1.2113369774349[/C][C]0.00246302256509904[/C][/ROW]
[ROW][C]7[/C][C]1.2266[/C][C]1.22402216873766[/C][C]0.00257783126233848[/C][/ROW]
[ROW][C]8[/C][C]1.2176[/C][C]1.21980668121891[/C][C]-0.00220668121890916[/C][/ROW]
[ROW][C]9[/C][C]1.2218[/C][C]1.23706949615962[/C][C]-0.0152694961596155[/C][/ROW]
[ROW][C]10[/C][C]1.249[/C][C]1.27453684858549[/C][C]-0.0255368485854857[/C][/ROW]
[ROW][C]11[/C][C]1.2991[/C][C]1.32185009115036[/C][C]-0.022750091150364[/C][/ROW]
[ROW][C]12[/C][C]1.3408[/C][C]1.35414384586336[/C][C]-0.0133438458633645[/C][/ROW]
[ROW][C]13[/C][C]1.3119[/C][C]1.31728716118542[/C][C]-0.00538716118542392[/C][/ROW]
[ROW][C]14[/C][C]1.3014[/C][C]1.30572885131265[/C][C]-0.00432885131265439[/C][/ROW]
[ROW][C]15[/C][C]1.3201[/C][C]1.32930360100527[/C][C]-0.0092036010052659[/C][/ROW]
[ROW][C]16[/C][C]1.2938[/C][C]1.29951097102213[/C][C]-0.00571097102213468[/C][/ROW]
[ROW][C]17[/C][C]1.2694[/C][C]1.26840355767614[/C][C]0.00099644232385885[/C][/ROW]
[ROW][C]18[/C][C]1.2165[/C][C]1.19863762334962[/C][C]0.0178623766503821[/C][/ROW]
[ROW][C]19[/C][C]1.2037[/C][C]1.20882563028746[/C][C]-0.00512563028746252[/C][/ROW]
[ROW][C]20[/C][C]1.2292[/C][C]1.2217384035693[/C][C]0.00746159643070151[/C][/ROW]
[ROW][C]21[/C][C]1.2256[/C][C]1.2084275633038[/C][C]0.0171724366961998[/C][/ROW]
[ROW][C]22[/C][C]1.2015[/C][C]1.20599780341479[/C][C]-0.00449780341478747[/C][/ROW]
[ROW][C]23[/C][C]1.1786[/C][C]1.19290586365931[/C][C]-0.014305863659309[/C][/ROW]
[ROW][C]24[/C][C]1.1856[/C][C]1.20684641585163[/C][C]-0.0212464158516276[/C][/ROW]
[ROW][C]25[/C][C]1.2103[/C][C]1.22941109890411[/C][C]-0.0191110989041095[/C][/ROW]
[ROW][C]26[/C][C]1.1938[/C][C]1.21517502327348[/C][C]-0.0213750232734798[/C][/ROW]
[ROW][C]27[/C][C]1.202[/C][C]1.22029270739015[/C][C]-0.0182927073901468[/C][/ROW]
[ROW][C]28[/C][C]1.2271[/C][C]1.25361034490699[/C][C]-0.0265103449069861[/C][/ROW]
[ROW][C]29[/C][C]1.277[/C][C]1.2672552113522[/C][C]0.00974478864779688[/C][/ROW]
[ROW][C]30[/C][C]1.265[/C][C]1.27867417592856[/C][C]-0.0136741759285636[/C][/ROW]
[ROW][C]31[/C][C]1.2684[/C][C]1.29025114512316[/C][C]-0.0218511451231586[/C][/ROW]
[ROW][C]32[/C][C]1.2811[/C][C]1.28923598215654[/C][C]-0.00813598215653775[/C][/ROW]
[ROW][C]33[/C][C]1.2727[/C][C]1.2782453767965[/C][C]-0.0055453767964963[/C][/ROW]
[ROW][C]34[/C][C]1.2611[/C][C]1.27056526597626[/C][C]-0.00946526597625876[/C][/ROW]
[ROW][C]35[/C][C]1.2881[/C][C]1.29820416521781[/C][C]-0.0101041652178125[/C][/ROW]
[ROW][C]36[/C][C]1.3213[/C][C]1.33367783630432[/C][C]-0.0123778363043212[/C][/ROW]
[ROW][C]37[/C][C]1.2999[/C][C]1.31143178402088[/C][C]-0.0115317840208796[/C][/ROW]
[ROW][C]38[/C][C]1.3074[/C][C]1.3150581049497[/C][C]-0.00765810494970298[/C][/ROW]
[ROW][C]39[/C][C]1.3242[/C][C]1.32560133543074[/C][C]-0.00140133543073699[/C][/ROW]
[ROW][C]40[/C][C]1.3516[/C][C]1.36182074543011[/C][C]-0.0102207454301063[/C][/ROW]
[ROW][C]41[/C][C]1.3511[/C][C]1.36762870084684[/C][C]-0.016528700846843[/C][/ROW]
[ROW][C]42[/C][C]1.3419[/C][C]1.35147071575872[/C][C]-0.00957071575871508[/C][/ROW]
[ROW][C]43[/C][C]1.3716[/C][C]1.37758960792583[/C][C]-0.00598960792582953[/C][/ROW]
[ROW][C]44[/C][C]1.3622[/C][C]1.33248095229446[/C][C]0.0297190477055439[/C][/ROW]
[ROW][C]45[/C][C]1.3896[/C][C]1.35667587207428[/C][C]0.0329241279257248[/C][/ROW]
[ROW][C]46[/C][C]1.4227[/C][C]1.40523526725053[/C][C]0.0174647327494681[/C][/ROW]
[ROW][C]47[/C][C]1.4684[/C][C]1.42879610455455[/C][C]0.0396038954454518[/C][/ROW]
[ROW][C]48[/C][C]1.457[/C][C]1.42849197922441[/C][C]0.0285080207755869[/C][/ROW]
[ROW][C]49[/C][C]1.4718[/C][C]1.44566938471601[/C][C]0.0261306152839947[/C][/ROW]
[ROW][C]50[/C][C]1.4748[/C][C]1.44929658092373[/C][C]0.0255034190762692[/C][/ROW]
[ROW][C]51[/C][C]1.5527[/C][C]1.51243377955687[/C][C]0.040266220443133[/C][/ROW]
[ROW][C]52[/C][C]1.575[/C][C]1.56320098668455[/C][C]0.0117990133154547[/C][/ROW]
[ROW][C]53[/C][C]1.5557[/C][C]1.5447915492392[/C][C]0.0109084507608035[/C][/ROW]
[ROW][C]54[/C][C]1.5553[/C][C]1.56209329968667[/C][C]-0.00679329968666991[/C][/ROW]
[ROW][C]55[/C][C]1.577[/C][C]1.57567394676499[/C][C]0.00132605323500831[/C][/ROW]
[ROW][C]56[/C][C]1.4975[/C][C]1.51160008502139[/C][C]-0.014100085021391[/C][/ROW]
[ROW][C]57[/C][C]1.437[/C][C]1.431491847964[/C][C]0.00550815203600369[/C][/ROW]
[ROW][C]58[/C][C]1.3322[/C][C]1.26970056659449[/C][C]0.06249943340551[/C][/ROW]
[ROW][C]59[/C][C]1.2732[/C][C]1.23484260071555[/C][C]0.0383573992844526[/C][/ROW]
[ROW][C]60[/C][C]1.3449[/C][C]1.33575394139797[/C][C]0.00914605860203141[/C][/ROW]
[ROW][C]61[/C][C]1.3239[/C][C]1.34205569496705[/C][C]-0.0181556949670507[/C][/ROW]
[ROW][C]62[/C][C]1.2785[/C][C]1.26082774772681[/C][C]0.0176722522731932[/C][/ROW]
[ROW][C]63[/C][C]1.305[/C][C]1.35323561906589[/C][C]-0.0482356190658892[/C][/ROW]
[ROW][C]64[/C][C]1.319[/C][C]1.35832962694587[/C][C]-0.0393296269458735[/C][/ROW]
[ROW][C]65[/C][C]1.365[/C][C]1.38467117177227[/C][C]-0.0196711717722702[/C][/ROW]
[ROW][C]66[/C][C]1.4016[/C][C]1.37505728298716[/C][C]0.0265427170128407[/C][/ROW]
[ROW][C]67[/C][C]1.4088[/C][C]1.37131906237019[/C][C]0.0374809376298102[/C][/ROW]
[ROW][C]68[/C][C]1.4268[/C][C]1.41633411239209[/C][C]0.0104658876079136[/C][/ROW]
[ROW][C]69[/C][C]1.4562[/C][C]1.46023734019676[/C][C]-0.00403734019676425[/C][/ROW]
[ROW][C]70[/C][C]1.4816[/C][C]1.50271227210132[/C][C]-0.0211122721013212[/C][/ROW]
[ROW][C]71[/C][C]1.4914[/C][C]1.48300669888353[/C][C]0.00839330111647364[/C][/ROW]
[ROW][C]72[/C][C]1.4614[/C][C]1.45819202989774[/C][C]0.00320797010226181[/C][/ROW]
[ROW][C]73[/C][C]1.4272[/C][C]1.43152942923821[/C][C]-0.00432942923821397[/C][/ROW]
[ROW][C]74[/C][C]1.3686[/C][C]1.3697665239768[/C][C]-0.00116652397680068[/C][/ROW]
[ROW][C]75[/C][C]1.3569[/C][C]1.41026201393869[/C][C]-0.0533620139386886[/C][/ROW]
[ROW][C]76[/C][C]1.3406[/C][C]1.38638486575738[/C][C]-0.0457848657573813[/C][/ROW]
[ROW][C]77[/C][C]1.2565[/C][C]1.27614011702264[/C][C]-0.0196401170226437[/C][/ROW]
[ROW][C]78[/C][C]1.2208[/C][C]1.21048527703335[/C][C]0.0103147229666521[/C][/ROW]
[ROW][C]79[/C][C]1.277[/C][C]1.26263051307406[/C][C]0.0143694869259367[/C][/ROW]
[ROW][C]80[/C][C]1.2894[/C][C]1.25025681829628[/C][C]0.0391431817037234[/C][/ROW]
[ROW][C]81[/C][C]1.3067[/C][C]1.29835743409813[/C][C]0.00834256590186939[/C][/ROW]
[ROW][C]82[/C][C]1.3898[/C][C]1.3841843910889[/C][C]0.00561560891110238[/C][/ROW]
[ROW][C]83[/C][C]1.3661[/C][C]1.338185664373[/C][C]0.027914335627001[/C][/ROW]
[ROW][C]84[/C][C]1.322[/C][C]1.32489256611531[/C][C]-0.00289256611531468[/C][/ROW]
[ROW][C]85[/C][C]1.336[/C][C]1.33245423517978[/C][C]0.003545764820222[/C][/ROW]
[ROW][C]86[/C][C]1.3649[/C][C]1.35502874189206[/C][C]0.00987125810793545[/C][/ROW]
[ROW][C]87[/C][C]1.3999[/C][C]1.40309910426324[/C][C]-0.00319910426324288[/C][/ROW]
[ROW][C]88[/C][C]1.4442[/C][C]1.46728349425332[/C][C]-0.0230834942533212[/C][/ROW]
[ROW][C]89[/C][C]1.4349[/C][C]1.44881895639264[/C][C]-0.0139189563926349[/C][/ROW]
[ROW][C]90[/C][C]1.4388[/C][C]1.4651356300705[/C][C]-0.0263356300705027[/C][/ROW]
[ROW][C]91[/C][C]1.4264[/C][C]1.44957173678859[/C][C]-0.023171736788594[/C][/ROW]
[ROW][C]92[/C][C]1.4343[/C][C]1.44000641701975[/C][C]-0.00570641701975462[/C][/ROW]
[ROW][C]93[/C][C]1.377[/C][C]1.35629897815892[/C][C]0.0207010218410776[/C][/ROW]
[ROW][C]94[/C][C]1.3706[/C][C]1.33881340426704[/C][C]0.0317865957329578[/C][/ROW]
[ROW][C]95[/C][C]1.3556[/C][C]1.3134405144273[/C][C]0.0421594855726951[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160475&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160475&T=4

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.26131.2716674045655-0.0103674045654983
21.26461.257327172519960.00727282748004476
31.22621.214293973140640.0119060268593566
41.19851.174321284071930.024178715928074
51.20071.21687705252504-0.0161770525250446
61.21381.21133697743490.00246302256509904
71.22661.224022168737660.00257783126233848
81.21761.21980668121891-0.00220668121890916
91.22181.23706949615962-0.0152694961596155
101.2491.27453684858549-0.0255368485854857
111.29911.32185009115036-0.022750091150364
121.34081.35414384586336-0.0133438458633645
131.31191.31728716118542-0.00538716118542392
141.30141.30572885131265-0.00432885131265439
151.32011.32930360100527-0.0092036010052659
161.29381.29951097102213-0.00571097102213468
171.26941.268403557676140.00099644232385885
181.21651.198637623349620.0178623766503821
191.20371.20882563028746-0.00512563028746252
201.22921.22173840356930.00746159643070151
211.22561.20842756330380.0171724366961998
221.20151.20599780341479-0.00449780341478747
231.17861.19290586365931-0.014305863659309
241.18561.20684641585163-0.0212464158516276
251.21031.22941109890411-0.0191110989041095
261.19381.21517502327348-0.0213750232734798
271.2021.22029270739015-0.0182927073901468
281.22711.25361034490699-0.0265103449069861
291.2771.26725521135220.00974478864779688
301.2651.27867417592856-0.0136741759285636
311.26841.29025114512316-0.0218511451231586
321.28111.28923598215654-0.00813598215653775
331.27271.2782453767965-0.0055453767964963
341.26111.27056526597626-0.00946526597625876
351.28811.29820416521781-0.0101041652178125
361.32131.33367783630432-0.0123778363043212
371.29991.31143178402088-0.0115317840208796
381.30741.3150581049497-0.00765810494970298
391.32421.32560133543074-0.00140133543073699
401.35161.36182074543011-0.0102207454301063
411.35111.36762870084684-0.016528700846843
421.34191.35147071575872-0.00957071575871508
431.37161.37758960792583-0.00598960792582953
441.36221.332480952294460.0297190477055439
451.38961.356675872074280.0329241279257248
461.42271.405235267250530.0174647327494681
471.46841.428796104554550.0396038954454518
481.4571.428491979224410.0285080207755869
491.47181.445669384716010.0261306152839947
501.47481.449296580923730.0255034190762692
511.55271.512433779556870.040266220443133
521.5751.563200986684550.0117990133154547
531.55571.54479154923920.0109084507608035
541.55531.56209329968667-0.00679329968666991
551.5771.575673946764990.00132605323500831
561.49751.51160008502139-0.014100085021391
571.4371.4314918479640.00550815203600369
581.33221.269700566594490.06249943340551
591.27321.234842600715550.0383573992844526
601.34491.335753941397970.00914605860203141
611.32391.34205569496705-0.0181556949670507
621.27851.260827747726810.0176722522731932
631.3051.35323561906589-0.0482356190658892
641.3191.35832962694587-0.0393296269458735
651.3651.38467117177227-0.0196711717722702
661.40161.375057282987160.0265427170128407
671.40881.371319062370190.0374809376298102
681.42681.416334112392090.0104658876079136
691.45621.46023734019676-0.00403734019676425
701.48161.50271227210132-0.0211122721013212
711.49141.483006698883530.00839330111647364
721.46141.458192029897740.00320797010226181
731.42721.43152942923821-0.00432942923821397
741.36861.3697665239768-0.00116652397680068
751.35691.41026201393869-0.0533620139386886
761.34061.38638486575738-0.0457848657573813
771.25651.27614011702264-0.0196401170226437
781.22081.210485277033350.0103147229666521
791.2771.262630513074060.0143694869259367
801.28941.250256818296280.0391431817037234
811.30671.298357434098130.00834256590186939
821.38981.38418439108890.00561560891110238
831.36611.3381856643730.027914335627001
841.3221.32489256611531-0.00289256611531468
851.3361.332454235179780.003545764820222
861.36491.355028741892060.00987125810793545
871.39991.40309910426324-0.00319910426324288
881.44421.46728349425332-0.0230834942533212
891.43491.44881895639264-0.0139189563926349
901.43881.4651356300705-0.0263356300705027
911.42641.44957173678859-0.023171736788594
921.43431.44000641701975-0.00570641701975462
931.3771.356298978158920.0207010218410776
941.37061.338813404267040.0317865957329578
951.35561.31344051442730.0421594855726951






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
145.83358958391742e-061.16671791678348e-050.999994166410416
159.44373502678206e-081.88874700535641e-070.99999990556265
161.39645793135457e-092.79291586270913e-090.999999998603542
174.64812482193756e-119.29624964387513e-110.999999999953519
186.75133010993233e-131.35026602198647e-120.999999999999325
191.75073799994854e-103.50147599989709e-100.999999999824926
208.24913493841431e-061.64982698768286e-050.999991750865062
213.27864166440295e-066.55728332880589e-060.999996721358336
226.12530454219033e-071.22506090843807e-060.999999387469546
231.46666345984402e-072.93332691968803e-070.999999853333654
242.8361275997801e-085.67225519956019e-080.999999971638724
258.61065409476097e-091.72213081895219e-080.999999991389346
263.34703614393602e-096.69407228787204e-090.999999996652964
276.46414428997695e-101.29282885799539e-090.999999999353586
281.43005876198153e-102.86011752396306e-100.999999999856994
292.58814949823141e-115.17629899646283e-110.999999999974118
306.31319058544716e-121.26263811708943e-110.999999999993687
311.82673582444524e-123.65347164889049e-120.999999999998173
325.78421869427863e-131.15684373885573e-120.999999999999422
339.60466349653668e-131.92093269930734e-120.99999999999904
342.08601875404632e-124.17203750809264e-120.999999999997914
356.06864119996496e-121.21372823999299e-110.999999999993931
361.20645522167669e-112.41291044335338e-110.999999999987935
374.78676761471718e-129.57353522943436e-120.999999999995213
383.83096589939402e-127.66193179878803e-120.999999999996169
392.48081372782524e-104.96162745565047e-100.999999999751919
402.40523005562857e-094.81046011125714e-090.99999999759477
412.3828532769692e-084.7657065539384e-080.999999976171467
425.91957091257177e-081.18391418251435e-070.999999940804291
436.47871060606201e-071.2957421212124e-060.999999352128939
440.0007834479659143460.001566895931828690.999216552034086
450.02285011018121290.04570022036242590.977149889818787
460.03678799242041030.07357598484082070.96321200757959
470.4443620303971890.8887240607943780.555637969602811
480.6228849582962980.7542300834074050.377115041703702
490.8993440800433280.2013118399133440.100655919956672
500.9569031922354070.08619361552918550.0430968077645927
510.9765177334971950.04696453300560990.0234822665028049
520.9877157161348680.0245685677302640.012284283865132
530.9850651794509830.02986964109803360.0149348205490168
540.9926139407490650.01477211850187030.00738605925093517
550.991733124733750.01653375053249980.00826687526624989
560.9933247560704380.01335048785912420.00667524392956211
570.9921694176115270.01566116477694580.00783058238847291
580.995699346554310.008601306891379970.00430065344568998
590.9971011858878230.005797628224353870.00289881411217693
600.9998745511923820.0002508976152357090.000125448807617854
610.9998802532969120.0002394934061769950.000119746703088497
620.9999173822832190.0001652354335613948.26177167806968e-05
630.9999611608554887.76782890236701e-053.88391445118351e-05
640.9999814011060333.71977879333135e-051.85988939666567e-05
650.9999890856089032.18287821948979e-051.0914391097449e-05
660.9999919164252281.61671495435345e-058.08357477176724e-06
670.9999879120192272.41759615466913e-051.20879807733457e-05
680.999974294502275.14109954609004e-052.57054977304502e-05
690.9999293896823150.0001412206353690567.06103176845278e-05
700.9998474535019610.0003050929960780370.000152546498039019
710.9996286620799640.0007426758400728730.000371337920036436
720.9994550277820550.001089944435889150.000544972217944574
730.9987380956856960.002523808628606920.00126190431430346
740.9968631211768540.006273757646291780.00313687882314589
750.9945318151808640.01093636963827170.00546818481913583
760.9873335273363230.02533294532735360.0126664726636768
770.9849828757597210.03003424848055790.0150171242402789
780.9731162529673450.05376749406530920.0268837470326546
790.9821104943524580.03577901129508430.0178895056475421
800.9713551684313570.05728966313728690.0286448315686434
810.974860164394240.05027967121151950.0251398356057597

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
14 & 5.83358958391742e-06 & 1.16671791678348e-05 & 0.999994166410416 \tabularnewline
15 & 9.44373502678206e-08 & 1.88874700535641e-07 & 0.99999990556265 \tabularnewline
16 & 1.39645793135457e-09 & 2.79291586270913e-09 & 0.999999998603542 \tabularnewline
17 & 4.64812482193756e-11 & 9.29624964387513e-11 & 0.999999999953519 \tabularnewline
18 & 6.75133010993233e-13 & 1.35026602198647e-12 & 0.999999999999325 \tabularnewline
19 & 1.75073799994854e-10 & 3.50147599989709e-10 & 0.999999999824926 \tabularnewline
20 & 8.24913493841431e-06 & 1.64982698768286e-05 & 0.999991750865062 \tabularnewline
21 & 3.27864166440295e-06 & 6.55728332880589e-06 & 0.999996721358336 \tabularnewline
22 & 6.12530454219033e-07 & 1.22506090843807e-06 & 0.999999387469546 \tabularnewline
23 & 1.46666345984402e-07 & 2.93332691968803e-07 & 0.999999853333654 \tabularnewline
24 & 2.8361275997801e-08 & 5.67225519956019e-08 & 0.999999971638724 \tabularnewline
25 & 8.61065409476097e-09 & 1.72213081895219e-08 & 0.999999991389346 \tabularnewline
26 & 3.34703614393602e-09 & 6.69407228787204e-09 & 0.999999996652964 \tabularnewline
27 & 6.46414428997695e-10 & 1.29282885799539e-09 & 0.999999999353586 \tabularnewline
28 & 1.43005876198153e-10 & 2.86011752396306e-10 & 0.999999999856994 \tabularnewline
29 & 2.58814949823141e-11 & 5.17629899646283e-11 & 0.999999999974118 \tabularnewline
30 & 6.31319058544716e-12 & 1.26263811708943e-11 & 0.999999999993687 \tabularnewline
31 & 1.82673582444524e-12 & 3.65347164889049e-12 & 0.999999999998173 \tabularnewline
32 & 5.78421869427863e-13 & 1.15684373885573e-12 & 0.999999999999422 \tabularnewline
33 & 9.60466349653668e-13 & 1.92093269930734e-12 & 0.99999999999904 \tabularnewline
34 & 2.08601875404632e-12 & 4.17203750809264e-12 & 0.999999999997914 \tabularnewline
35 & 6.06864119996496e-12 & 1.21372823999299e-11 & 0.999999999993931 \tabularnewline
36 & 1.20645522167669e-11 & 2.41291044335338e-11 & 0.999999999987935 \tabularnewline
37 & 4.78676761471718e-12 & 9.57353522943436e-12 & 0.999999999995213 \tabularnewline
38 & 3.83096589939402e-12 & 7.66193179878803e-12 & 0.999999999996169 \tabularnewline
39 & 2.48081372782524e-10 & 4.96162745565047e-10 & 0.999999999751919 \tabularnewline
40 & 2.40523005562857e-09 & 4.81046011125714e-09 & 0.99999999759477 \tabularnewline
41 & 2.3828532769692e-08 & 4.7657065539384e-08 & 0.999999976171467 \tabularnewline
42 & 5.91957091257177e-08 & 1.18391418251435e-07 & 0.999999940804291 \tabularnewline
43 & 6.47871060606201e-07 & 1.2957421212124e-06 & 0.999999352128939 \tabularnewline
44 & 0.000783447965914346 & 0.00156689593182869 & 0.999216552034086 \tabularnewline
45 & 0.0228501101812129 & 0.0457002203624259 & 0.977149889818787 \tabularnewline
46 & 0.0367879924204103 & 0.0735759848408207 & 0.96321200757959 \tabularnewline
47 & 0.444362030397189 & 0.888724060794378 & 0.555637969602811 \tabularnewline
48 & 0.622884958296298 & 0.754230083407405 & 0.377115041703702 \tabularnewline
49 & 0.899344080043328 & 0.201311839913344 & 0.100655919956672 \tabularnewline
50 & 0.956903192235407 & 0.0861936155291855 & 0.0430968077645927 \tabularnewline
51 & 0.976517733497195 & 0.0469645330056099 & 0.0234822665028049 \tabularnewline
52 & 0.987715716134868 & 0.024568567730264 & 0.012284283865132 \tabularnewline
53 & 0.985065179450983 & 0.0298696410980336 & 0.0149348205490168 \tabularnewline
54 & 0.992613940749065 & 0.0147721185018703 & 0.00738605925093517 \tabularnewline
55 & 0.99173312473375 & 0.0165337505324998 & 0.00826687526624989 \tabularnewline
56 & 0.993324756070438 & 0.0133504878591242 & 0.00667524392956211 \tabularnewline
57 & 0.992169417611527 & 0.0156611647769458 & 0.00783058238847291 \tabularnewline
58 & 0.99569934655431 & 0.00860130689137997 & 0.00430065344568998 \tabularnewline
59 & 0.997101185887823 & 0.00579762822435387 & 0.00289881411217693 \tabularnewline
60 & 0.999874551192382 & 0.000250897615235709 & 0.000125448807617854 \tabularnewline
61 & 0.999880253296912 & 0.000239493406176995 & 0.000119746703088497 \tabularnewline
62 & 0.999917382283219 & 0.000165235433561394 & 8.26177167806968e-05 \tabularnewline
63 & 0.999961160855488 & 7.76782890236701e-05 & 3.88391445118351e-05 \tabularnewline
64 & 0.999981401106033 & 3.71977879333135e-05 & 1.85988939666567e-05 \tabularnewline
65 & 0.999989085608903 & 2.18287821948979e-05 & 1.0914391097449e-05 \tabularnewline
66 & 0.999991916425228 & 1.61671495435345e-05 & 8.08357477176724e-06 \tabularnewline
67 & 0.999987912019227 & 2.41759615466913e-05 & 1.20879807733457e-05 \tabularnewline
68 & 0.99997429450227 & 5.14109954609004e-05 & 2.57054977304502e-05 \tabularnewline
69 & 0.999929389682315 & 0.000141220635369056 & 7.06103176845278e-05 \tabularnewline
70 & 0.999847453501961 & 0.000305092996078037 & 0.000152546498039019 \tabularnewline
71 & 0.999628662079964 & 0.000742675840072873 & 0.000371337920036436 \tabularnewline
72 & 0.999455027782055 & 0.00108994443588915 & 0.000544972217944574 \tabularnewline
73 & 0.998738095685696 & 0.00252380862860692 & 0.00126190431430346 \tabularnewline
74 & 0.996863121176854 & 0.00627375764629178 & 0.00313687882314589 \tabularnewline
75 & 0.994531815180864 & 0.0109363696382717 & 0.00546818481913583 \tabularnewline
76 & 0.987333527336323 & 0.0253329453273536 & 0.0126664726636768 \tabularnewline
77 & 0.984982875759721 & 0.0300342484805579 & 0.0150171242402789 \tabularnewline
78 & 0.973116252967345 & 0.0537674940653092 & 0.0268837470326546 \tabularnewline
79 & 0.982110494352458 & 0.0357790112950843 & 0.0178895056475421 \tabularnewline
80 & 0.971355168431357 & 0.0572896631372869 & 0.0286448315686434 \tabularnewline
81 & 0.97486016439424 & 0.0502796712115195 & 0.0251398356057597 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160475&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]14[/C][C]5.83358958391742e-06[/C][C]1.16671791678348e-05[/C][C]0.999994166410416[/C][/ROW]
[ROW][C]15[/C][C]9.44373502678206e-08[/C][C]1.88874700535641e-07[/C][C]0.99999990556265[/C][/ROW]
[ROW][C]16[/C][C]1.39645793135457e-09[/C][C]2.79291586270913e-09[/C][C]0.999999998603542[/C][/ROW]
[ROW][C]17[/C][C]4.64812482193756e-11[/C][C]9.29624964387513e-11[/C][C]0.999999999953519[/C][/ROW]
[ROW][C]18[/C][C]6.75133010993233e-13[/C][C]1.35026602198647e-12[/C][C]0.999999999999325[/C][/ROW]
[ROW][C]19[/C][C]1.75073799994854e-10[/C][C]3.50147599989709e-10[/C][C]0.999999999824926[/C][/ROW]
[ROW][C]20[/C][C]8.24913493841431e-06[/C][C]1.64982698768286e-05[/C][C]0.999991750865062[/C][/ROW]
[ROW][C]21[/C][C]3.27864166440295e-06[/C][C]6.55728332880589e-06[/C][C]0.999996721358336[/C][/ROW]
[ROW][C]22[/C][C]6.12530454219033e-07[/C][C]1.22506090843807e-06[/C][C]0.999999387469546[/C][/ROW]
[ROW][C]23[/C][C]1.46666345984402e-07[/C][C]2.93332691968803e-07[/C][C]0.999999853333654[/C][/ROW]
[ROW][C]24[/C][C]2.8361275997801e-08[/C][C]5.67225519956019e-08[/C][C]0.999999971638724[/C][/ROW]
[ROW][C]25[/C][C]8.61065409476097e-09[/C][C]1.72213081895219e-08[/C][C]0.999999991389346[/C][/ROW]
[ROW][C]26[/C][C]3.34703614393602e-09[/C][C]6.69407228787204e-09[/C][C]0.999999996652964[/C][/ROW]
[ROW][C]27[/C][C]6.46414428997695e-10[/C][C]1.29282885799539e-09[/C][C]0.999999999353586[/C][/ROW]
[ROW][C]28[/C][C]1.43005876198153e-10[/C][C]2.86011752396306e-10[/C][C]0.999999999856994[/C][/ROW]
[ROW][C]29[/C][C]2.58814949823141e-11[/C][C]5.17629899646283e-11[/C][C]0.999999999974118[/C][/ROW]
[ROW][C]30[/C][C]6.31319058544716e-12[/C][C]1.26263811708943e-11[/C][C]0.999999999993687[/C][/ROW]
[ROW][C]31[/C][C]1.82673582444524e-12[/C][C]3.65347164889049e-12[/C][C]0.999999999998173[/C][/ROW]
[ROW][C]32[/C][C]5.78421869427863e-13[/C][C]1.15684373885573e-12[/C][C]0.999999999999422[/C][/ROW]
[ROW][C]33[/C][C]9.60466349653668e-13[/C][C]1.92093269930734e-12[/C][C]0.99999999999904[/C][/ROW]
[ROW][C]34[/C][C]2.08601875404632e-12[/C][C]4.17203750809264e-12[/C][C]0.999999999997914[/C][/ROW]
[ROW][C]35[/C][C]6.06864119996496e-12[/C][C]1.21372823999299e-11[/C][C]0.999999999993931[/C][/ROW]
[ROW][C]36[/C][C]1.20645522167669e-11[/C][C]2.41291044335338e-11[/C][C]0.999999999987935[/C][/ROW]
[ROW][C]37[/C][C]4.78676761471718e-12[/C][C]9.57353522943436e-12[/C][C]0.999999999995213[/C][/ROW]
[ROW][C]38[/C][C]3.83096589939402e-12[/C][C]7.66193179878803e-12[/C][C]0.999999999996169[/C][/ROW]
[ROW][C]39[/C][C]2.48081372782524e-10[/C][C]4.96162745565047e-10[/C][C]0.999999999751919[/C][/ROW]
[ROW][C]40[/C][C]2.40523005562857e-09[/C][C]4.81046011125714e-09[/C][C]0.99999999759477[/C][/ROW]
[ROW][C]41[/C][C]2.3828532769692e-08[/C][C]4.7657065539384e-08[/C][C]0.999999976171467[/C][/ROW]
[ROW][C]42[/C][C]5.91957091257177e-08[/C][C]1.18391418251435e-07[/C][C]0.999999940804291[/C][/ROW]
[ROW][C]43[/C][C]6.47871060606201e-07[/C][C]1.2957421212124e-06[/C][C]0.999999352128939[/C][/ROW]
[ROW][C]44[/C][C]0.000783447965914346[/C][C]0.00156689593182869[/C][C]0.999216552034086[/C][/ROW]
[ROW][C]45[/C][C]0.0228501101812129[/C][C]0.0457002203624259[/C][C]0.977149889818787[/C][/ROW]
[ROW][C]46[/C][C]0.0367879924204103[/C][C]0.0735759848408207[/C][C]0.96321200757959[/C][/ROW]
[ROW][C]47[/C][C]0.444362030397189[/C][C]0.888724060794378[/C][C]0.555637969602811[/C][/ROW]
[ROW][C]48[/C][C]0.622884958296298[/C][C]0.754230083407405[/C][C]0.377115041703702[/C][/ROW]
[ROW][C]49[/C][C]0.899344080043328[/C][C]0.201311839913344[/C][C]0.100655919956672[/C][/ROW]
[ROW][C]50[/C][C]0.956903192235407[/C][C]0.0861936155291855[/C][C]0.0430968077645927[/C][/ROW]
[ROW][C]51[/C][C]0.976517733497195[/C][C]0.0469645330056099[/C][C]0.0234822665028049[/C][/ROW]
[ROW][C]52[/C][C]0.987715716134868[/C][C]0.024568567730264[/C][C]0.012284283865132[/C][/ROW]
[ROW][C]53[/C][C]0.985065179450983[/C][C]0.0298696410980336[/C][C]0.0149348205490168[/C][/ROW]
[ROW][C]54[/C][C]0.992613940749065[/C][C]0.0147721185018703[/C][C]0.00738605925093517[/C][/ROW]
[ROW][C]55[/C][C]0.99173312473375[/C][C]0.0165337505324998[/C][C]0.00826687526624989[/C][/ROW]
[ROW][C]56[/C][C]0.993324756070438[/C][C]0.0133504878591242[/C][C]0.00667524392956211[/C][/ROW]
[ROW][C]57[/C][C]0.992169417611527[/C][C]0.0156611647769458[/C][C]0.00783058238847291[/C][/ROW]
[ROW][C]58[/C][C]0.99569934655431[/C][C]0.00860130689137997[/C][C]0.00430065344568998[/C][/ROW]
[ROW][C]59[/C][C]0.997101185887823[/C][C]0.00579762822435387[/C][C]0.00289881411217693[/C][/ROW]
[ROW][C]60[/C][C]0.999874551192382[/C][C]0.000250897615235709[/C][C]0.000125448807617854[/C][/ROW]
[ROW][C]61[/C][C]0.999880253296912[/C][C]0.000239493406176995[/C][C]0.000119746703088497[/C][/ROW]
[ROW][C]62[/C][C]0.999917382283219[/C][C]0.000165235433561394[/C][C]8.26177167806968e-05[/C][/ROW]
[ROW][C]63[/C][C]0.999961160855488[/C][C]7.76782890236701e-05[/C][C]3.88391445118351e-05[/C][/ROW]
[ROW][C]64[/C][C]0.999981401106033[/C][C]3.71977879333135e-05[/C][C]1.85988939666567e-05[/C][/ROW]
[ROW][C]65[/C][C]0.999989085608903[/C][C]2.18287821948979e-05[/C][C]1.0914391097449e-05[/C][/ROW]
[ROW][C]66[/C][C]0.999991916425228[/C][C]1.61671495435345e-05[/C][C]8.08357477176724e-06[/C][/ROW]
[ROW][C]67[/C][C]0.999987912019227[/C][C]2.41759615466913e-05[/C][C]1.20879807733457e-05[/C][/ROW]
[ROW][C]68[/C][C]0.99997429450227[/C][C]5.14109954609004e-05[/C][C]2.57054977304502e-05[/C][/ROW]
[ROW][C]69[/C][C]0.999929389682315[/C][C]0.000141220635369056[/C][C]7.06103176845278e-05[/C][/ROW]
[ROW][C]70[/C][C]0.999847453501961[/C][C]0.000305092996078037[/C][C]0.000152546498039019[/C][/ROW]
[ROW][C]71[/C][C]0.999628662079964[/C][C]0.000742675840072873[/C][C]0.000371337920036436[/C][/ROW]
[ROW][C]72[/C][C]0.999455027782055[/C][C]0.00108994443588915[/C][C]0.000544972217944574[/C][/ROW]
[ROW][C]73[/C][C]0.998738095685696[/C][C]0.00252380862860692[/C][C]0.00126190431430346[/C][/ROW]
[ROW][C]74[/C][C]0.996863121176854[/C][C]0.00627375764629178[/C][C]0.00313687882314589[/C][/ROW]
[ROW][C]75[/C][C]0.994531815180864[/C][C]0.0109363696382717[/C][C]0.00546818481913583[/C][/ROW]
[ROW][C]76[/C][C]0.987333527336323[/C][C]0.0253329453273536[/C][C]0.0126664726636768[/C][/ROW]
[ROW][C]77[/C][C]0.984982875759721[/C][C]0.0300342484805579[/C][C]0.0150171242402789[/C][/ROW]
[ROW][C]78[/C][C]0.973116252967345[/C][C]0.0537674940653092[/C][C]0.0268837470326546[/C][/ROW]
[ROW][C]79[/C][C]0.982110494352458[/C][C]0.0357790112950843[/C][C]0.0178895056475421[/C][/ROW]
[ROW][C]80[/C][C]0.971355168431357[/C][C]0.0572896631372869[/C][C]0.0286448315686434[/C][/ROW]
[ROW][C]81[/C][C]0.97486016439424[/C][C]0.0502796712115195[/C][C]0.0251398356057597[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160475&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160475&T=5

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
145.83358958391742e-061.16671791678348e-050.999994166410416
159.44373502678206e-081.88874700535641e-070.99999990556265
161.39645793135457e-092.79291586270913e-090.999999998603542
174.64812482193756e-119.29624964387513e-110.999999999953519
186.75133010993233e-131.35026602198647e-120.999999999999325
191.75073799994854e-103.50147599989709e-100.999999999824926
208.24913493841431e-061.64982698768286e-050.999991750865062
213.27864166440295e-066.55728332880589e-060.999996721358336
226.12530454219033e-071.22506090843807e-060.999999387469546
231.46666345984402e-072.93332691968803e-070.999999853333654
242.8361275997801e-085.67225519956019e-080.999999971638724
258.61065409476097e-091.72213081895219e-080.999999991389346
263.34703614393602e-096.69407228787204e-090.999999996652964
276.46414428997695e-101.29282885799539e-090.999999999353586
281.43005876198153e-102.86011752396306e-100.999999999856994
292.58814949823141e-115.17629899646283e-110.999999999974118
306.31319058544716e-121.26263811708943e-110.999999999993687
311.82673582444524e-123.65347164889049e-120.999999999998173
325.78421869427863e-131.15684373885573e-120.999999999999422
339.60466349653668e-131.92093269930734e-120.99999999999904
342.08601875404632e-124.17203750809264e-120.999999999997914
356.06864119996496e-121.21372823999299e-110.999999999993931
361.20645522167669e-112.41291044335338e-110.999999999987935
374.78676761471718e-129.57353522943436e-120.999999999995213
383.83096589939402e-127.66193179878803e-120.999999999996169
392.48081372782524e-104.96162745565047e-100.999999999751919
402.40523005562857e-094.81046011125714e-090.99999999759477
412.3828532769692e-084.7657065539384e-080.999999976171467
425.91957091257177e-081.18391418251435e-070.999999940804291
436.47871060606201e-071.2957421212124e-060.999999352128939
440.0007834479659143460.001566895931828690.999216552034086
450.02285011018121290.04570022036242590.977149889818787
460.03678799242041030.07357598484082070.96321200757959
470.4443620303971890.8887240607943780.555637969602811
480.6228849582962980.7542300834074050.377115041703702
490.8993440800433280.2013118399133440.100655919956672
500.9569031922354070.08619361552918550.0430968077645927
510.9765177334971950.04696453300560990.0234822665028049
520.9877157161348680.0245685677302640.012284283865132
530.9850651794509830.02986964109803360.0149348205490168
540.9926139407490650.01477211850187030.00738605925093517
550.991733124733750.01653375053249980.00826687526624989
560.9933247560704380.01335048785912420.00667524392956211
570.9921694176115270.01566116477694580.00783058238847291
580.995699346554310.008601306891379970.00430065344568998
590.9971011858878230.005797628224353870.00289881411217693
600.9998745511923820.0002508976152357090.000125448807617854
610.9998802532969120.0002394934061769950.000119746703088497
620.9999173822832190.0001652354335613948.26177167806968e-05
630.9999611608554887.76782890236701e-053.88391445118351e-05
640.9999814011060333.71977879333135e-051.85988939666567e-05
650.9999890856089032.18287821948979e-051.0914391097449e-05
660.9999919164252281.61671495435345e-058.08357477176724e-06
670.9999879120192272.41759615466913e-051.20879807733457e-05
680.999974294502275.14109954609004e-052.57054977304502e-05
690.9999293896823150.0001412206353690567.06103176845278e-05
700.9998474535019610.0003050929960780370.000152546498039019
710.9996286620799640.0007426758400728730.000371337920036436
720.9994550277820550.001089944435889150.000544972217944574
730.9987380956856960.002523808628606920.00126190431430346
740.9968631211768540.006273757646291780.00313687882314589
750.9945318151808640.01093636963827170.00546818481913583
760.9873335273363230.02533294532735360.0126664726636768
770.9849828757597210.03003424848055790.0150171242402789
780.9731162529673450.05376749406530920.0268837470326546
790.9821104943524580.03577901129508430.0178895056475421
800.9713551684313570.05728966313728690.0286448315686434
810.974860164394240.05027967121151950.0251398356057597






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level480.705882352941177NOK
5% type I error level600.882352941176471NOK
10% type I error level650.955882352941177NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 48 & 0.705882352941177 & NOK \tabularnewline
5% type I error level & 60 & 0.882352941176471 & NOK \tabularnewline
10% type I error level & 65 & 0.955882352941177 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160475&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]48[/C][C]0.705882352941177[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]60[/C][C]0.882352941176471[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]65[/C][C]0.955882352941177[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160475&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160475&T=6

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level480.705882352941177NOK
5% type I error level600.882352941176471NOK
10% type I error level650.955882352941177NOK


Figures (Output of Computation)

Figure 1
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}